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|a UAMI
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|a Semi-supervised learning :
|b background, applications and future directions /
|c Guoqiang Zhong and Kaizhu Huang editors.
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|a New York :
|b Nova Science Publishers,
|c [2018]
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|c ©2018
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|a 1 online resource (241 pages)
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|a text
|b txt
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|a computer
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|2 rdamedia
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|a online resource
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|a Education in a competitive and globalizing world
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|a Includes bibliographical references and index.
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|a Print version record.
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|a Intro; SEMI-SUPERVISED LEARNINGBACKGROUND, APPLICATIONSAND FUTURE DIRECTIONS; SEMI-SUPERVISED LEARNINGBACKGROUND, APPLICATIONSAND FUTURE DIRECTIONS; CONTENTS; PREFACE; Introduction to This Book; Target Audience; Acknowledgments; Chapter 1CONSTRAINED DATASELF-REPRESENTATIVE GRAPHCONSTRUCTION; Abstract; 1. Introduction; 2. Constrained Data Self-Representative GraphConstruction; 3. Kernelized Variants; 3.1. Hilbert Space; 3.2. Column Generation; 4. Performance Evaluation; 4.1. Label Propagation; 4.1.1. Gaussian Random Fields; 4.1.2. Local and Global Consistency; 4.2. Experimental Results
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|a 4.2.1. Comparison among Several Graph Construction Methods4.2.2. Stability of the Proposed Method; 4.2.3. Sensitivity to Parameters; 4.2.4. Computational Complexity and CPU Time; Acknowledgments; Conclusion; References; Chapter 2INJECTING RANDOMNESS INTO GRAPHS:AN ENSEMBLE SEMI-SUPERVISEDLEARNING FRAMEWORK; Abstract; 1. Introduction; 2. Background; 2.1. Graph-Based Semi-Supervised Learning; 2.2. Ensemble Learning and Random Forests; 2.3. Anchor Graph; 3. Random Multi-Graphs; 3.1. Problem Formulation; 3.2. Algorithm; 3.3. Graph Construction; 3.4. Semi-Supervised Inference
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|a 3.5. Inductive Extension3.6. Randomness as Regularization; 4. Experiments; 4.1. Data Sets; 4.2. Experimental Results; 4.3. Impact of Parameters; 4.4. Hyperspectral Image Classification; Acknowledgments; Conclusion; References; Chapter 3LABEL PROPAGATION VIA KERNELFLEXIBLE MANIFOLD EMBEDDING; Abstract; 1. Introduction; 2. RelatedWork; 2.1. Semi-Supervised Discriminant Analysis; 2.2. Semi-Supervised Discriminant Embedding; 2.3. Laplacian Regularized Least Square; 2.4. Review of the Flexible Manifold Embedding Framework; 3. Kernel FlexibleManifold Embedding; 3.1. The Objective Function
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|a 3.2. Optimal Solution3.3. The Algorithm; 3.4. Difference between KFME and Existing Methods; 3.4.1. Difference between KFME and FME; 3.4.2. Difference between KFME and Other Methods; 4. Experimental Results; 4.1. Datasets; 4.2. Method Comparison; 4.3. Results Analysis; 4.4. Stability with Respect to Graph; Acknowledgments; Conclusion; References; Chapter 4FAST GRAPH-BASED SEMI-SUPERVISEDLEARNING AND ITS APPLICATIONS; Abstract; 1. Introduction; 2. Related Work; 2.1. Scalable Graph-Based SSL/TL Methods; 2.2. Scalable Graph Construction Methods; 2.3. Robust Graph-Based SSL/TL Methods
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|a 3. Minimum Tree Cut Method3.1. Notations; 3.2. The Proposed Method; 3.3. The Tree Labeling Algorithm; 3.4. Generate a Spanning Tree from a Graph; 4. Insensitiveness to Graph Construction; 5. Experiments; 5.1. Data Set; 5.1.1. UCI Data Set; 5.1.2. Image; 5.1.3. Text; 5.2. Graph Construction; 5.3. Accuracy; 5.4. Speed; 5.5. Robustness; 5.6. Effect of Different Spanning Tree and Ensemble of MultipleSpanning Trees; 6. Applications in Text Extraction; 6.1. Interactive Text Extraction in Natural Scene Images; 6.2. Document Image Binarization; Conclusion and FutureWork; References
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|a eBooks on EBSCOhost
|b EBSCO eBook Subscription Academic Collection - Worldwide
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|a Supervised learning (Machine learning)
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|a Zhong, Guoqiang,
|e editor.
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|a Huang, Kaizhu,
|e editor.
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|i Print version:
|a Zhong, Guoqiang.
|t Semi-Supervised Learning: Background, Applications and Future Directions.
|d New York : Nova Science Publishers, Incorporated, ©2018
|z 9781536135565
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|a Education in a competitive and globalizing world series.
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